Low-Cost and Fast Epiretinal Membrane Detection and Quantification Based on SD-OCT

Epiretinal membrane (ERM) is a pathological condition characterized by the formation of a non-vascularized fibrocellular membrane on the inner retinal surface, leading to retinal traction, macular edema, and visual impairment. Optical coherence tomography (OCT) is the primary imaging modality for diagnosing macular ERM, enabling high-resolution visualization of retinal morphology and thickness. Although the transition from Time-Domain (TD-OCT) to Spectral-Domain OCT (SD-OCT) has significantly improved clinical imaging, the latest advancement-Swept-Source OCT (SS-OCT)-remains limited in clinical adoption due to its high cost, complexity, and processing demands. Consequently, many clinics are unable to leverage the intuitive en face visualization and quantitative analysis of ERM provided by SS-OCT. In this study, we propose a novel and cost-effective pipeline for ERM detection and quantification using only SD-OCT B-scans. Our method introduces a new en face projection technique, termed <i>Epiretinal Projection Image (EPI)</i>, which enables intuitive visualization of ERM spatial distribution. By leveraging a YOLOv11x-based deep learning model, we achieve high-precision ERM detection on B-scan images (mAP@50: 0.882, mAP@50:95: 0.556) and accurately project detected regions onto the EPI map for objective area quantification. Furthermore, we propose an association scoring mechanism that correlates EPI projections with retinal thickness maps, revealing a strong spatial relationship (correlation score: 0.771) between ERM presence and retinal thickening-a type of analysis not feasible even with SS-OCT. In the clinical reliability and expert acceptability evaluations conducted by board-certified retina specialists, the results demonstrated excellent inter-expert agreement (ICC = 0.94, <i>κ</i> = 0.89), with an average agreement score (AAS) of 4.77 and an acceptance rate (AR, scores ≥ 4) of 93.3%). These findings indicate a strong alignment between the model outputs and real-world clinical judgment. Our findings demonstrate that the proposed system provides accurate and interpretable ERM localization and quantitative assessment using widely available SD-OCT devices, suggesting its potential for reliable clinical application pending further large-scale validation.